code: def test_types_are_correct(parameters, results):
      gpyopt = GPyOpt(max_concurrent=1)
      suggestion = gpyopt.get_suggestion(parameters, results, True)
      assert isinstance(suggestion["dropout"], float)
      assert isinstance(suggestion["lr"], float)
      assert isinstance(suggestion["num_hidden"], int)
      assert isinstance(suggestion["activation"], str)
  
imports: import sherpa
  import pandas
  import pytest
  import numpy
  import collections
  import GPyOpt
  from sherpa.algorithms.bayesian_optimization import GPyOpt
  from sherpa.algorithms import Repeat
label_desc: Use image node_id 15 for calling a global function with example usage: test_types_are_correct(parameters, results) without return types